RESEARCH: INFLUENZA
FOLDING PROJECT #18470 PROFILE
PROJECT TEAM
Manager(s): Dylan NovackInstitution: Temple University
Project URL: View Project Website
WORK UNIT INFO
Atoms: 93,425Core: 0xa8
Status: Public
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TLDR; PROJECT SUMMARY AI BETA
Miniproteins are tiny drugs that can fight infections. Scientists are using computer simulations to understand how miniproteins bind to viruses like the flu. They hope this will help them design even better miniprotein drugs in the future.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Designed miniproteins are a class of biomolecules with intermediate sizes—larger than small-molecule drugs, but smaller than monoclonal antibodies.
Miniproteins can be computationally designed to tightly bind protein targets for use as potential therapeutics, a promising new avenue for treating infectious disease. Hemagglutinin is a viral fusion protein that allows H1 influenza A (HA) to bind sialic acid on cell surfaces, as well as being involved in the post-endocytosis mechanism of cellular infection.
The Baker lab at University of Washington has developed de novo designed miniproteins that bind hemagglutinin, and improved their binding through affinity maturation (Chevalier et al.
2017).
Many of the mutations seen in affinity-matured sequences are not found in the binding interface, and it remains an open question how these changes lead to higher affinity.
Furthermore, many of the computational predictions of how single-point mutations affect binding deviate significantly from the experimentally determined values. Could all-atom molecular simulation approaches achieve more accurate predictions? In this set of simulations, we aim to use massively parallel expanded ensemble simulations to predict mutational effects on affinities to hemagglutinin.
By pairing these simulations with other simulations aimed at modeling the binding reactions of these miniproteins to hemagglutinin, we aim to have a relatively complete picture of a miniprotein-target binding reaction and how mutations affect it.
These studies are a large-scale investigation on how miniprotein binding reactions work in atomic detail, towards a better understanding of computational design and modulation of miniprotein therapeutics.
RELATED TERMS GLOSSARY AI BETA
Miniproteins
Small proteins with therapeutic potential.
Miniproteins are a class of engineered proteins designed to be smaller than traditional antibodies. They have the potential to treat various diseases by binding to specific targets in the body. Their small size allows for better penetration into tissues and potentially fewer side effects compared to larger drugs.
Hemagglutinin
A viral protein that binds to sialic acid on cell surfaces.
Hemagglutinin is a crucial protein found on the surface of influenza viruses. It allows the virus to attach to and enter host cells by binding to sialic acid molecules present on the cell membrane. This binding process initiates infection and enables the virus to spread.
Monoclonal Antibodies
Laboratory-produced antibodies that target a specific antigen.
Monoclonal antibodies are a type of engineered antibody designed to recognize and bind to a single specific target, known as an antigen. They are widely used in treating various diseases by targeting cancerous cells, blocking viral infections, or modulating immune responses.
Affinity Maturation
Process of improving the binding affinity of a molecule to its target.
Affinity maturation is a technique used in drug development to enhance the binding strength between a therapeutic molecule and its target. It involves introducing random mutations into the molecule's structure and selecting those with improved binding affinity.
Molecular Simulation
Computer-based modeling of molecular interactions.
Molecular simulation involves using computer programs to simulate the behavior of molecules and their interactions. This technique is widely used in drug discovery to predict how molecules will bind to their targets and understand the mechanisms underlying biological processes.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 03:28:34|
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PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 03:28:34|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|---|---|---|---|---|
| 1 | RYZEN 9 7950X 16-CORE | 32 | 30,612 | 979,584 | AMD |
| 2 | RYZEN 7 7700X 8-CORE | 16 | 33,795 | 540,720 | AMD |
| 3 | RYZEN 9 5900X 12-CORE | 24 | 22,297 | 535,128 | AMD |
| 4 | RYZEN 7 5800X3D 8-CORE | 16 | 29,953 | 479,248 | AMD |
| 5 | RYZEN 9 5950X 16-CORE | 32 | 11,849 | 379,168 | AMD |
| 6 | RYZEN 7 5700X 8-CORE | 16 | 20,157 | 322,512 | AMD |
| 7 | 12TH GEN CORE I5-12600K | 16 | 19,455 | 311,280 | Intel |
| 8 | RYZEN 7 5700G | 16 | 16,986 | 271,776 | AMD |
| 9 | RYZEN 7 5800X 8-CORE | 16 | 15,859 | 253,744 | AMD |
| 10 | 12TH GEN CORE I7-12700 | 20 | 11,691 | 233,820 | Intel |
| 11 | 11TH GEN CORE I9-11900K @ 3.50GHZ | 16 | 14,375 | 230,000 | Intel |
| 12 | CORE I9-7940X CPU @ 3.10GHZ | 28 | 7,091 | 198,548 | Intel |
| 13 | RYZEN 7 3700X 8-CORE | 16 | 6,809 | 108,944 | AMD |
| 14 | CORE I7-10700T CPU @ 2.00GHZ | 16 | 5,273 | 84,368 | Intel |
| 15 | 12TH GEN CORE I7-1270P | 16 | 3,121 | 49,936 | Intel |